ANALISIS SISTEM DETEKSI SERANGAN DDOS ADAPTIF MENGGUNAKAN METODE RT-AMD PADA INFRASTRUKTUR CLOUD COMPUTING

MAHARANI, SAHARA DIVA and Heryanto, Ahmad (2024) ANALISIS SISTEM DETEKSI SERANGAN DDOS ADAPTIF MENGGUNAKAN METODE RT-AMD PADA INFRASTRUKTUR CLOUD COMPUTING. Undergraduate thesis, Sriwijaya University.

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Abstract

Distributed Denial of Service (DDoS) is a major threat to cloud computing infrastructure, capable of causing service disruptions and significant losses. This study aims to develop an adaptive DDoS attack detection system using the RT-AMD (Real-Time Attack Monitoring and Detection) method, which combines four machine learning algorithms: Decision Tree, Random Forest, Naive Bayes, and K-Nearest Neighbors, to enhance detection accuracy and efficiency. The research utilizes the CICDDoS2019 dataset, focusing on SYN Flood and UDP Flood attacks. Evaluation results indicate that the Decision Tree method achieves an accuracy of 95.6%, Random Forest 98.4%, Naive Bayes 88.7%, and K-Nearest Neighbors 93.2%. The system demonstrates its effectiveness in adaptively detecting DDoS attacks, efficiently utilizing resources, and identifying attack patterns in real-time. Thus, this study significantly contributes to the development of smarter and more adaptive detection methods to improve the security of cloud computing networks while serving as a reference for future research in the field of cybersecurity.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	DDoS, RT-AMD, Machine Learning, Cloud Computing, Keamanan Jaringan
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Sahara Diva Maharani
Date Deposited:	14 Jan 2025 01:34
Last Modified:	14 Jan 2025 01:34
URI:	http://repository.unsri.ac.id/id/eprint/164380

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